Multi-sector determinants of implementation and sustainment for non-specialist treatment of depression and post-traumatic stress disorder in Kenya: a concept mapping study
Bibliographic record
Abstract
BACKGROUND: The global shortage of trained mental health workers disproportionately impacts mental health care access in low- and middle-income countries. In Kenya, effective strategies are needed to scale-up the workforce to meet the demand for depression and post-traumatic stress disorder treatment. Task-shifting - delegating specific tasks to non-specialist workers - is one workforce expansion approach. However, non-specialist workers remain underutilized in Kenya due to a paucity of research on how to scale-up and sustain such service models. METHODS: Purposive sampling was used to recruit experts from policy, healthcare practice, research, and mental health advocacy roles in Kenya (N = 30). Participants completed concept mapping activities to explore factors likely to facilitate or hinder a collaborative Ministry of Health-researcher training of the mental health non-specialist workforce. Participants brainstormed 71 statements describing determinants and implementation strategies, sorted and rated the importance and changeability of each. Multidimensional scaling and hierarchical cluster analysis quantified relationships between statements. The Exploration, Preparation, Implementation, and Sustainment (EPIS) framework guided cluster interpretation activities. RESULTS: Twelve determinant clusters were identified: 1) Current workforce characteristics, 2) Exploration considerations, 3) Preparation considerations, 4) Sustainment considerations, 5) Inner context implementation processes and tools, 6) Local capacity and partnerships, 7) Financing for community health teams, 8) Outer context resource allocation/policy into action, 9) Workforce characteristics to enhance during implementation, 10) Workforce implementation strategies, 11) Cross-level workforce strategies, and 12) Training and education recommendations. Cluster 8 was rated the most important and changeable. CONCLUSION: Concept mapping offers a rapid, community-engaged approach for identifying determinants and implementation strategies to address workforce shortages. Organizing results by EPIS phases can help prioritize strategy deployment to achieve implementation goals. Scale-up and sustainment of the non-specialist workforce in Kenya requires formal partnerships between the Ministry of Health and community health worker teams to distribute financial resources and collaboratively standardize training curriculum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".